The paper introduces CCLab, an adversarial testing framework, to systematically evaluate the robustness of both learning-based and traditional congestion controllers, finding that learning-based contr…

This paper presents AlphaRoute, a multi-objective adaptive search framework for VLSI global routing using Large Language Models as semantic policy optimizers.

This paper proposes a value-based DCC method for Intelligent Transport Systems to maintain channel load while retaining more high-value objects.

This paper characterizes the impact of ETSI Decentralized Congestion Control (DCC) on age of information (AoI) for vehicle-to-infrastructure updates, revealing a hyperbolic density dependence and prop…

This paper proposes CAPS, a scheduling layer for data centers that separates rate computation and packet scheduling, reducing queue occupancy by up to 10x without throughput loss.

This paper proposes the Parameter-Efficient Hybrid Transformer (PEHT) framework for network traffic prediction in urban cellular networks, which integrates mobility and congestion information, reduces…

This paper compares Google's BBR-v3 Congestion Control Algorithm to eight others over SpaceX's Starlink network, demonstrating its fairness and throughput maximization in high-latency, variable satell…

This paper introduces Stigmergic Graph Memory (SGM), a method to improve warehouse throughput in many-to-many Multi-Agent Pickup and Delivery (MAPD) by using a bounded, decaying memory layer to record…

The paper presents teLLMe, a system for exploratory causal analysis of urban driving datasets using structured event tables, causal structure learning, and query-specific effect estimation.

The ZCube topology, which eliminates path multiplicity and reduces switching hardware, delivers better performance for large model training and inference than traditional multipath datacenter networks…

The paper introduces Optimal Mixture Transport (OMT), a scalable framework that reformulates optimal transport by using mixtures of subpopulations, resulting in a unique, biconvex optimization problem…

A framework is proposed to classify bugs and evaluate clinical equivalence of AID system software updates.

This paper proves that a majority of filled-in Sudoku grids require a logarithmic fraction of cells to be filled by clues, and constructs grids requiring 18 and 80 clues for 9x9 and 16x16 Sudoku, resp…

This paper introduces CoughPhase-CLR, a self-supervised learning framework for cough representation learning using physiological phases, outperforming standard techniques on five downstream tasks.

This paper introduces Stale Synchronous Parallel mode of execution for parallel sparse triangular linear system solve and presents a scheduler that achieves geometric-mean speed-ups of 7-30% over Grow…

This paper analyzes darknet traffic to characterize advanced, AI-assisted bot reconnaissance, finding that modern evasion techniques allow most bot traffic to bypass standard IDS thresholds.

The paper empirically investigates the lead marketing ecosystem, revealing a highly non-compliant system that aggressively collects, shares, and monetizes sensitive personal data through deceptive bro…

The paper introduces EvoXXLTraffic, an ultra-large, sensor-evolving dataset that simulates real-world road network growth, demonstrating that existing state-of-the-art traffic forecasting models fail…